The present disclosure relates generally to motion compensated temporal filtering (MCTF) for open loop scalable video coding, and particularly to MCTF employing prediction and update processes.
Motion Compensated Temporal Filtering (MCTF) has shown to be a very efficient tool for open loop scalable video coding as it enables open loop video coding that provides for quality scalability. The efficiency of MCTF in video coding has been recognized by standardization committees, such as MPEG (Motion Picture Experts Group). The MCTF process is separated into two sub-processes: an update process and a prediction process. Contrary to hybrid video coding, current implementations of the MCTF principle require the use of “residual buffers” in the motion compensation steps, as the picture must be updated before being used for the prediction process. As such, one of the drawbacks of the MCTF process is that additional decoding picture buffers (residual buffers that store the predicted pictures) are needed to store intermediate decoded pictures. Residual buffers introduce some practical problems as they require higher precision than those used for output pictures and updated pictures (that is, conventional picture buffers), placing higher memory demands and complicating buffer management in low-complexity implementations. A remedy to this problem is to remove totally the update process. However this introduces a penalty in compression efficiency and degrades the quality of the decoded video. Another remedy is to use two conventional picture buffers to store each residual picture, and modify the MCTF process to be able to split the residual information into these two buffers. Although this solution solves the problem of having two different types of buffers with differing precisions, it roughly doubles the amount of memory required to store residual pictures. Accordingly, there is a need in the art of MCTF that overcomes these drawbacks.